Distrusting Consensus: How a Uniform Corona Pandemic Narrative Fostered Suspicion and Conspiracy Theories
Bibliographic record
Abstract
Although the institutional model of science communication operated well during the corona-pandemic, and relevant public institutions (media, science, politics) garnered higher levels of trust following “rally-around-the-flag” dynamics, other people would develop distrusts towards those institutions and the emerging orthodox corona narrative. Their ideas are often framed as conspiracy theories, and today’s globalized media eco-system enables their proliferation. This looming “infodemic” became a prime object of concern. In this article I agnostically study those distrusts from a cultural sociological perspective to better understand how and why people (came to) disbelieve official knowledge and their producers. To do so, I draw on my ethnographic fieldwork in the off- and online worlds of people labeled as conspiracy theorists in the Netherlands, which includes the media they consume, share and produce. Based on an inductive analysis of people’s own sense-making, I present three dominant reasons: media’s panicky narrative of fear and mayhem; governments sole focus on lockdowns and vaccines; and the exclusion of heterodox scientific perspectives in the public sphere. Each of these reasons problematize a perceived orthodoxy in media, politics and science, and this uniformity bred suspicion about possible conspiracies between these public institutions. Too much consensus gets distrusted. While we can discard those ideas as irrational conspiracy theories, I conclude that these findings have important implications for the way we deal with and communicate about complex societal problems. Next to keeping things simple and clear, as crisis/risk/science communication holds, we need to allow for uncertainty, critique and epistemic diversity as well.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.065 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".